Simultaneous capture of 3D surface and 2D intensity information of a scene can provide valuable information in many applications. For example, a biometric system may rely on the computation of traditional biometrics to achieve identity verification from 2D intensity images via iris and retina recognition while simultaneously performing 3D face recognition. In another example, a video-based traffic enforcement system may achieve automatic license plate recognition (ALPR) from 2D intensity images and vehicle classification or speed estimation from 3D surface data. In healthcare applications, monitoring cardiac and respiratory events is of clinical importance in the early detection of potentially fatal conditions. Current technologies involve contact sensors the individual must wear constantly. Such a requirement can lead to patient discomfort, dependency, loss of dignity, and further may fail due to a variety of reasons including refusal to wear the monitoring device. Elderly patients are even more likely to suffer from the adverse effects of continued monitoring. Unobtrusive, non-contact, imaging based methods are increasingly needed for monitoring patients. One such system uses a single channel camera under structured and unstructured illuminations to capture video of a subject of interest such that the system can monitor both the cardiac and respiratory events. The system isolates pixels associated with the subject's vascular pathways within each frame from the image frames comprising pixels with intensity values corresponding to detected reflected energy projected by unstructured illumination source and estimates subject's chest volume by reconstructing 3D depth map from the image frames comprising pixels corresponding to detected reflected energy projected by structured illumination source. This system requires the simultaneous projection of structured and unstructured light sources. However, artifacts due to the interference caused by the use of structured light patterns have arisen. Such artifacts can adversely impact on the 3D surface reconstruction and spatial feature extraction. For example, FIG. 1 shows an image of a random structured light pattern projected onto a subject's hand. Pixels from the projected dot pattern of the structured illumination source can alter the extracted underlying vascular patterns even when interpolated with values of surrounding pixels, especially when the blood vessels therein are narrow or short and the projected dot pattern has a relatively high density. This can negatively impact cardiac/respiratory data derived from information associated with pixels of the subject's blood vessels.
Accordingly, what is needed in this art are increasingly sophisticated methods for reconstructing images captured of a scene being illuminated with unstructured and structured illumination sources.